The logistics and transport indicators provide key insights into the efficiency of the transportation sector across various modes. Essential for evaluating the movement of goods and people, these indicators assess infrastructure health and industry performance. Key metrics, such as air passengers, freight, carrier departures, port traffic, and railway lines reflect specific aspects such as demand, volume, capacity, and development. Monitoring these indicators enables stakeholders to optimize logistics, make informed decisions, and identify industry trends. However, the domain faces challenges influenced by regulations and trends such as air travel demand, e-commerce, technological advancements, and sustainability. Successfully navigating these challenges requires adapting strategies, embracing innovation, and prioritizing sustainability to ensure future success in the ever-evolving logistics and transport indicators domain.
Data coverage:
The dataset encompasses data from 152 countries. The charts depict the situation of each country in six different domains. These domains are socioeconomic indicators, macroeconomic indicators, health indicators, digital and connectivity indicators, consumption indicators, as well as logistics and transport indicators. Within these domains, various segments are covered, including demography, economic measures, economic inequality, employment, consumption, health determinants, and much more.
Modeling approach:
The composition of each domain follows a comprehensive approach that combines both top-down and bottom-up methodologies, with each domain and segment being guided by a specific rationale. To evaluate the situation of these six domains within each country, we rely on pertinent indicators and data from reputable international institutions, local national statistical offices, industry associations, and leading private institutions. Additionally, we undertake data processing procedures to address issues such as missing timelines, outliers, and data inconsistency. Our data processing incorporates advanced statistical techniques, including interpolation, exponential moving weighted average, and the Savitzky-Golay filter. These methods contribute to the refinement and enhancement of data quality.
Forecasts:
In our forecasting process, a wide range of statistical techniques is utilized based on the characteristics of the markets. For example, the S-curve function is employed to forecast the adoption of new technology, products, and services, aligning the forecast model with the theory of innovation adoption. Additionally, the data is forecasted using ARIMA with and without seasonality considerations, exponential trend smoothing, and the Compound Annual Growth Rate (CAGR), with the option to incorporate adjustment factors when necessary. These techniques enable accurate and reliable forecast methods tailored to the unique characteristics of the data in each market and country.
Additional notes:
The data is updated twice per year or every time there is a significant change in their dynamics. The impacts of the COVID-19 pandemic and of the Russia/Ukraine war are considered at a country-specific level.
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